To what extent can neural network models learn generalizations about language structure,
and how do we find out what they have learned? We explore these questions by training …

Computational typology has gained traction in the field of Natural Language Processing
(NLP) in recent years, as evidenced by the increasing number of papers on the topic and the …

The neural revolution has redefined–and many would argue, undermined–the place of
traditional linguistics in natural language processing. The pace at which large unsupervised …

In semantic typology, colexification refers to words with multiple meanings, either related
(polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded …

This paper describes a method to enrich lexical resources with content relating to linguistic
diversity, based on knowledge from the field of lexical typology. We capture the …

Bridging the performance gap between high-and low-resource languages has been the
focus of much previous work. Typological features from databases such as the World Atlas of …

Byte-pair encoding (BPE) is widely used in NLP for performing subword tokenization. It
uncovers redundant patterns for compressing the data, and hence alleviates the sparsity …

Typological information has the potential to be beneficial in the development of NLP models,
particularly for low-resource languages. Unfortunately, current large-scale typological …

Colexification refers to the linguistic phenomenon where a single lexical form is used to
convey multiple meanings. By studying cross-lingual colexifications, researchers have …

While information from the field of linguistic typology has the potential to improve
performance on NLP tasks, reliable typological data is a prerequisite. Existing typological …